Vision-based pipette station automatic calibration method and equipment

Abstract

The invention provides a vision-based pipette station position automatic calibration method and equipment, and the method comprises the steps: carrying out vision internal calibration based on a calibration head, a target plate position and a camera visual field range, and obtaining a circle center pixel coordinate O2, a circle radius R1, a pipette station robot coordinate C1 and an offset offset between a teaching point and a reference coordinate; and performing visual calibration based on the pixel coordinate O2 of the circle center, the radius R1 of the circle, the coordinate C1 of the pipetting station robot and the offset offset between the teaching point and the reference coordinate to obtain a calibration value of the target plate position. According to the invention, the automatic calibration of the pipette site is realized, the operation steps are simplified, the error caused by manual misoperation is avoided, and the efficiency of the calibration process is improved.

Description

Technical Field

[0001] The invention relates to a vision-based automatic calibration method and device for a liquid transfer station. Background Art

[0002] In automated laboratories, pipetting station robots are widely used for high-throughput liquid handling operations such as sample transfer, reagent dispensing, and liquid mixing. These operations require extremely high precision and repeatability to ensure the reliability of experimental results. In order to achieve the required operational accuracy, the pipetting station usually needs to be accurately calibrated before the experiment to determine the reference coordinates of each plate position.

[0003] Currently, most pipetting stations rely on manual calibration to complete this process. Operators usually need to manually adjust and calibrate the crosshair coordinates of each plate position to ensure that the robot can accurately position itself during plate transfer and pipetting operations. The manual calibration process has the following significant disadvantages:

[0004] (1) Time-consuming and labor-intensive: The manual calibration process takes a lot of time, especially when dealing with multiple board positions. This not only requires the operator to participate for a long time, but may also lead to inefficiency due to human factors.

[0005] (2) Unstable accuracy: Since manual calibration relies on the operator's visual judgment and manual operation, it is easily affected by fatigue, distraction and personal operating habits, resulting in unstable calibration accuracy. Even for experienced operators, it is difficult to ensure that the same accuracy can be achieved every time the calibration is performed.

[0006] (3) High risk of error: Inaccurate operations during manual calibration may lead to error accumulation, affecting the accuracy of subsequent experimental operations. These errors are particularly obvious in experiments with high precision requirements, which may lead to deviations in experimental results or even failure. Summary of the invention

[0007] The object of the present invention is to provide a vision-based automatic calibration method and device for pipetting station positions.

[0008] In order to solve the above problems, the present invention provides a vision-based automatic calibration method for pipetting station positions, comprising:

[0009] Placing a calibration block with a built-in camera on a target plate position that needs to be calibrated in a pipetting station, wherein a crosshair reference coordinate is set on the target plate position;

[0010] A calibration head with a circular bottom is installed on the pipette, and the pipette with the calibration head is moved to a shooting range where the circular shape of the calibration head can be detected in the photo taken by the camera, which serves as the camera field of view;

[0011] Based on the calibration head, the target plate position and the camera field of view, a visual internal calibration is performed to obtain the pixel coordinates O2 of the center of the circle, the radius R1 of the circle, the coordinates C1 of the robot of the pipetting station and the offset Offset between the teaching point and the reference coordinates;

[0012] Based on the pixel coordinates O2 of the center of the circle, the radius R1 of the circle, the coordinates C1 of the robot of the pipetting station, and the offset Offset between the teaching point and the reference coordinates, visual calibration is performed to obtain the calibration value of the target plate position.

[0013] Furthermore, in the above method, based on the calibration head, the target plate position and the camera field of view, visual internal calibration is performed to obtain the pixel coordinates O2 of the center of the circle, the radius R1 of the circle, the coordinates C1 of the robot of the pipetting station and the offset Offset between the teaching point and the reference coordinates, including:

[0014] Keeping the Z axis unchanged, moving the X axis and the Y axis, moving the pipette gun with the calibration head to 9 different first positions within the field of view of the camera, using the camera on the calibration block to take 9 first images corresponding to each first position, and recording the coordinates of the pipetting station robot corresponding to the 9 different first positions;

[0015] The affine transformation matrix is ​​calculated by using the pixel coordinates of the center of the 9 first images and the corresponding 9 pipetting station robot coordinates;

[0016] Keeping the Z axis unchanged, move the pipette gun to any position within the field of view of the camera as a teaching point, capture a second image through the camera at the teaching point, and obtain the pixel coordinates O2 of the center of the circle at the bottom of the calibration head at the teaching point, the radius R1 of the circle, and the coordinates C1 of the pipetting station robot based on the second image; measure the distance from the center of the circle at the bottom of the calibration head to the camera;

[0017] Move the pipette gun to the position of the crosshair reference coordinate of the target plate position, and record the current pipetting station robot coordinate C2;

[0018] The coordinate deviation between the pipetting station robot coordinate C1 and the pipetting station robot coordinate C2 is calculated to obtain the offset Offset between the teaching point and the reference coordinate.

[0019] Furthermore, in the above method, the affine transformation matrix is ​​calculated by using the center pixel coordinates of the 9 first images and the corresponding 9 pipetting station robot coordinates, including:

[0020] Each pixel coordinate of the center of the circle corresponds to a pipetting station robot coordinate, and for each pair of points formed by each pixel coordinate of the center of the circle and the corresponding pipetting station robot coordinate, a set of linear equations is established to express the corresponding relationship between the pixel coordinates of the center of the circle and the coordinates of the pipetting station robot, wherein each pair of points corresponds to a set of linear equations, and 9 pairs of points will form 9 sets of linear equations;

[0021] Based on 9 sets of linear equations, the parameters of the affine transformation matrix are solved by the least squares method;

[0022] Based on the affine transformation matrix parameters, a 2x3 affine transformation matrix is ​​obtained to transform the pixel points in the image coordinate system into the pipetting station robot coordinate system.

[0023] Furthermore, in the above method, the parameters of the affine transformation matrix include six parameters of transformation coefficients of rotation, scaling and translation.

[0024] Furthermore, in the above method, the affine transformation matrix is ​​expressed as the following formula (1):

[0025]

[0026] Wherein, (x, y) is the pixel coordinate of the center of the first image; (x', y') is the robot coordinate of the pipetting station;

[0027] [a 11 ,a 12 ,a 21 ,a 22 ] is the linear part of the transformation matrix, which represents the transformation coefficients of rotation and scaling;

[0028] [t x ,t y ] is a translation vector, representing the translation transformation coefficient of the first image.

[0029] Furthermore, in the above method, based on the center pixel coordinate O2, the radius R1 of the circle, the pipetting station robot coordinate C1 and the offset Offset between the teaching point and the reference coordinate, visual calibration is performed to obtain the calibration value of the target plate position, including:

[0030] Step S40, selecting any position within the field of view of the camera as the current position of the pipette;

[0031] Step S41, at the current position of the pipette, use the camera in the calibration block to capture a third image;

[0032] Step S42, detecting the current radius R2 of the circle at the bottom of the calibration head in the third image by using the Hough circle detection algorithm;

[0033] Step S43, subtracting the radius R1 of the circle from the current radius R2 of the corresponding circle in the third image to obtain a radius deviation, if the radius deviation is greater than a preset radius deviation threshold, proceed to step S44, otherwise proceed to step S45;

[0034] Step S44, calculating the offset of the Z axis by the radius deviation, taking the position of the pipette after moving the pipette according to the offset of the Z axis as the current position of the pipette, and returning to step S41;

[0035] Step S45, at the current position of the pipette, use the camera in the calibration block to capture a fourth image;

[0036] Step S46, detecting the pixel coordinate O4 of the center of the circle at the bottom of the calibration head in the fourth image by using the Hough circle detection algorithm;

[0037] Step S47, subtracting the center pixel coordinate O2 from the center pixel coordinate O4 corresponding to the fourth image to obtain a pixel deviation. If the pixel deviation is greater than a preset pixel deviation threshold, proceed to step S48, otherwise proceed to step S49;

[0038] Step S48, converting the pixel deviation into a pipetting station robot coordinate offset through the affine transformation matrix, taking the position of the pipette after moving the pipette according to the pipetting station robot coordinate offset as the current position of the pipette, and returning to step S45;

[0039] Step S49, subtracting the offset Offset between the teaching point and the reference coordinate from the X-axis and Y-axis coordinates of the current position of the pipette gun to obtain the X-axis and Y-axis coordinates of the crosshair reference coordinates;

[0040] Step S50, subtract the Z-axis coordinate of the current position of the pipette gun from the Z-axis coordinate of the pipette station robot coordinate C1, and add the Z-axis coordinate of the pipette station robot coordinate C2 to obtain the Z-axis coordinate of the crosshair reference coordinate of the target plate position.

[0041] Furthermore, in the above method, in step S44, the Z-axis offset calculation formula is: (the distance from the center of the circle at the bottom of the calibration head to the camera*circle radius deviation / current radius R2).

[0042] Furthermore, in the above method, in step S48, the formula for converting the pixel deviation to the coordinate offset of the pipetting station robot is as follows:

[0043] dx′=a 11 dx+a 12 dy

[0044] dy′=a 21 dx+a 22 dy

[0045] Where (dx, dy) is the image pixel deviation; (dx', dy') is the coordinate offset of the pipetting station robot.

[0046] According to another aspect of the present invention, there is further provided a computer-readable storage medium having computer-executable instructions stored thereon, wherein when the computer-executable instructions are executed by a processor, the processor is caused to: execute any of the methods described above.

[0047] According to another aspect of the present invention, there is also provided a computer device, comprising:

[0048] Processor; and

[0049] A memory arranged to store computer executable instructions, which when executed cause the processor to: perform any of the methods described above.

[0050] Compared with the prior art, the vision-based automatic calibration method for the pipetting station provided by the present invention realizes the automatic calibration of the pipetting station through the calibration block with a built-in high-precision camera and the automatic calibration head. This automated method not only simplifies the operation steps, but also avoids the errors caused by human misoperation, and improves the efficiency of the calibration process.

[0051] The present invention uses the Hough circle detection algorithm and affine transformation matrix through precise visual internal calibration and visual calibration steps to accurately detect and calculate the relative position relationship between the calibration head and the camera. This high-precision visual detection method can effectively reduce the error in the calibration process, ensure the accuracy of each calibration point, and improve the working reliability of the entire pipetting station.

[0052] The present invention adopts an accurate visual calibration method, which can ensure that the results of each calibration are highly consistent. This consistency is particularly important for pipetting work that requires frequent calibration, and can effectively avoid experimental errors caused by inconsistent calibration, thereby improving the repeatability and reliability of experimental results. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a flow chart of a vision-based automatic calibration method according to an embodiment of the present invention;

[0054] Figure 2 It is a flow chart of visual internal calibration in a vision-based automatic calibration method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0056] In a typical configuration of the present application, the terminal, the device of the service network and the trusted party all include one or more processors (CPU), input / output interfaces, network interfaces and memories.

[0057] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0058] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include non-transitory media such as modulated data signals and carrier waves.

[0059] like Figure 1 As shown, the present invention provides a vision-based automatic calibration method for pipetting station positions, the method comprising:

[0060] Step S1, placing the camera: placing a calibration block with a built-in high-precision camera on a target plate position to be calibrated in a pipetting station, where a crosshair reference coordinate is set;

[0061] Step S2, installing the calibration head: installing a calibration head with a circular bottom on the pipette, and moving the pipette with the calibration head to a shooting range where the circular shape of the calibration head can be detected in the photo taken by the camera as the camera field of view;

[0062] Here, the camera field of view refers to the shooting range in which the circular shape of the calibration head can be detected in the photos taken by the camera;

[0063] like Figure 2As shown, step S3, visual internal calibration: based on the calibration head, the target plate position and the camera field of view, visual internal calibration is performed to obtain the pixel coordinates O2 of the center of the circle, the radius R1 of the circle, the coordinates C1 of the robot of the pipetting station and the offset Offset between the teaching point and the reference coordinates;

[0064] Here, if the visual internal calibration has been completed, skip this step;

[0065] Step S31, keep the Z axis unchanged, move the pipette gun and shoot: move the pipette gun with the calibration head to 9 different first positions within the field of view of the camera by moving the X axis and the Y axis, use the camera on the calibration block to respectively shoot 9 first images corresponding to each first position, and record the coordinates of the pipetting station robot corresponding to the 9 different first positions;

[0066] Step S32, detecting the coordinates of the center of the circle at the bottom of the calibration head in the nine first images: detecting the pixel coordinates of the center of the circle at the bottom of the calibration head in the nine first images by using a Hough circle detection algorithm;

[0067] Step S33, calculating an affine transformation matrix: calculating an affine transformation matrix through the center pixel coordinates of the 9 first images and the corresponding 9 pipetting station robot coordinates;

[0068] Here, the affine transformation matrix may be used in the subsequent step S48;

[0069] Preferably, step S33 includes: obtaining the affine transformation matrix:

[0070] Step S331, constructing a set of equations: corresponding each circle center pixel coordinate to a pipetting station robot coordinate, and for each pair of points formed by each circle center pixel coordinate and the corresponding pipetting station robot coordinate, establishing a set of linear equations to express the corresponding relationship between the circle center pixel coordinate and the pipetting station robot coordinate, wherein each pair of points corresponds to a set of linear equations, and 9 pairs of points will form 9 sets of linear equations;

[0071] Step S332, solving the transformation matrix: based on 9 sets of linear equations, and using the least square method, solving 6 parameters of the affine transformation matrix, the 6 parameters include: transformation coefficients of rotation, scaling and translation;

[0072] Step S333, obtaining an affine transformation matrix: Based on the six parameters of the affine transformation matrix, a 2x3 affine transformation matrix is ​​finally obtained, which can transform the pixel points in the image coordinate system into the pipetting station robot coordinate system.

[0073] Preferably, the affine transformation matrix is ​​expressed as the following formula (1):

[0074]

[0075] Wherein, (x, y) is the pixel coordinate of the center of the first image; (x', y') is the robot coordinate of the pipetting station;

[0076] [a 11 ,a 12 ,a 21 ,a 22 ] is the linear part of the transformation matrix, which represents the transformation coefficients of rotation and scaling operations;

[0077] [t x ,t y ] is a translation vector, representing the transformation coefficient of the translation operation of the first image.

[0078] Step S34, move to the teaching point and record data: keep the Z-axis value in step S31 unchanged, move the pipette gun with the calibration head to any position within the field of view of the camera as the teaching point through the movement of the X-axis and the Y-axis, take a second image through the camera at the teaching point, and obtain the pixel coordinates O2 of the center of the circle at the bottom of the calibration head at the teaching point, the radius R1 of the circle, and the coordinates C1 of the pipetting station robot based on the second image; measure the distance from the center of the circle at the bottom of the calibration head to the camera;

[0079] Here, the position of the teaching point may be one of the 9 different first positions, or any other position within the camera field of view;

[0080] The circle center pixel coordinate O2 may be used in the subsequent step S47; the radius R1 may be used in the subsequent step S43;

[0081] The distance from the center of the circle at the bottom of the calibration head to the camera can be used in the subsequent step S44. When measuring the distance from the center of the circle at the bottom of the calibration head to the camera, it can be measured manually, or the robot can be controlled to move the position of the teaching point for taking pictures to the camera lens position, and the Z-axis deviation after the movement is obtained as the distance from the center of the circle at the bottom of the calibration head to the camera in the visual internal calibration.

[0082] Step S35, move to the crosshair reference point and record data: move the pipette gun to the position of the crosshair reference coordinate of the target plate position, and record the current pipetting station robot coordinate C2;

[0083] Step S36, calculating the coordinate deviation: calculating the coordinate deviation between the pipetting station robot coordinate C1 and the pipetting station robot coordinate C2, and obtaining the offset Offset between the teaching point and the reference coordinate.

[0084] Step S4, visual calibration: Based on the circle center pixel coordinate O2, the circle radius R1, the pipetting station robot coordinate C1 and the offset Offset between the teaching point and the reference coordinate, visual calibration is performed to obtain the calibration value of the target plate position.

[0085] Step S40, selecting any position within the field of view of the high-precision camera as the current position of the pipette;

[0086] Step S41, taking a photo: at the current position of the pipette, use the camera in the calibration block to take a third image;

[0087] Step S42, detecting the radius of the circle in the third image: detecting the current radius R2 of the circle at the bottom of the calibration head in the third image by using the Hough circle detection algorithm;

[0088] Step S43, calculating the radius deviation: subtracting the radius R1 of the circle corresponding to the teaching point obtained in step S34 from the current radius R2 of the circle corresponding to the third image to obtain the radius deviation. If the radius deviation is greater than the preset radius deviation threshold, proceed to step S44, otherwise proceed to step S45;

[0089] Step S44, calculate the Z-axis offset and move the pipette: calculate the Z-axis offset by the radius deviation, and use the position of the pipette after moving the pipette according to the Z-axis offset as the current position of the pipette, and return to step S41;

[0090] Preferably, the Z-axis offset calculation formula is: (the distance from the center of the circle at the bottom of the calibration head to the camera * the circle radius deviation / the current radius R2), wherein the distance from the center of the circle at the bottom of the calibration head to the camera can be obtained by measurement in step S34;

[0091] Step S45, taking a picture: at the current position of the pipette, use the camera in the calibration block to take a fourth image;

[0092] Here, if the radius deviation in step S43 is less than or equal to the preset radius deviation threshold, the current position of the pipette in step S45 is the same as the current position of the pipette in step S41; if the radius deviation in step S43 is greater than the preset radius deviation threshold, the current position of the pipette in step S45 is the current position of the pipette after being moved by the offset of the Z axis;

[0093] Step S46, detecting the pixel coordinates of the center of the circle in the fourth image: using the Hough circle detection algorithm, detecting the pixel coordinates O4 of the center of the circle at the bottom of the calibration head in the fourth image;

[0094] Step S47, calculating the pixel deviation: subtracting the pixel coordinate O2 of the center of the teaching point obtained in step S34 from the pixel coordinate O4 of the center of the circle corresponding to the fourth image to obtain the pixel deviation. If the pixel deviation is greater than the preset pixel deviation threshold, proceed to step S48, otherwise proceed to step S49;

[0095] Step S48, calculate the XY axis offset and move the pipette: convert the pixel deviation into the pipette station robot coordinate offset through the affine transformation matrix obtained in step S33, and use the position of the pipette after moving the pipette according to the pipette station robot coordinate offset as the current position of the pipette, and then return to step S45 to continue iteration to improve accuracy;

[0096] Preferably, the formula for converting the pixel deviation to the coordinate offset of the pipetting station robot is as follows using the affine transformation matrix obtained in step S33:

[0097] dx′=a 11 dx+a 12 dy

[0098] dy′=a 21 dx+a 22 dy

[0099] Where (dx, dy) is the image pixel deviation; (dx', dy') is the coordinate offset of the pipetting station robot;

[0100] Here, after obtaining the image pixel deviations dx and dy, the linear part of the affine transformation matrix [a 11, a 12, a 21 ,a 22 ], the image pixel deviation can be converted to the pipetting station robot coordinate offsets dx' and dy'.

[0101] Step S49, calculating the XY axis coordinates of the reference point: subtracting the offset Offset between the teaching point and the reference coordinate obtained in step S36 from the X and Y axis coordinates of the current position of the pipette gun to obtain the X and Y axis coordinates of the crosshair reference coordinates;

[0102] Step S50, calculate the Z-axis coordinate of the reference point: subtract the Z-axis coordinate of the current position of the pipette gun from the Z-axis coordinate of the pipette station robot coordinate C1 obtained in step S34, and add the Z-axis coordinate of the pipette station robot coordinate C2 obtained in step S35 to obtain the Z-axis coordinate of the cross-line reference coordinate of the target plate position.

[0103] Here, the pipette can be controlled to move to the target plate position based on the X-axis, Y-axis and Z-axis coordinates of the crosshair reference coordinates.

[0104] According to another aspect of the present invention, there is further provided a computer-readable storage medium having computer-executable instructions stored thereon, wherein when the computer-executable instructions are executed by a processor, the processor is caused to: execute any of the methods described above.

[0105] According to another aspect of the present invention, there is also provided a computer device, comprising:

[0106] Processor; and

[0107] A memory arranged to store computer executable instructions, which when executed cause the processor to: perform any of the methods described above.

[0108] In summary, the vision-based automatic calibration method for the pipetting station provided by the present invention realizes the automatic calibration of the pipetting station through the calibration block with a built-in high-precision camera and the automatic calibration head. This automated method not only simplifies the operation steps, but also avoids the errors caused by human misoperation, thereby improving the efficiency of the calibration process.

[0109] The present invention uses the Hough circle detection algorithm and affine transformation matrix through precise visual internal calibration and visual calibration steps to accurately detect and calculate the relative position relationship between the calibration head and the camera. This high-precision visual detection method can effectively reduce the error in the calibration process, ensure the accuracy of each calibration point, and improve the working reliability of the entire pipetting station.

[0110] The present invention adopts an accurate visual calibration method, which can ensure that the results of each calibration are highly consistent. This consistency is particularly important for pipetting work that requires frequent calibration, and can effectively avoid experimental errors caused by inconsistent calibration, thereby improving the repeatability and reliability of experimental results.

[0111] The detailed contents of the device embodiments of the present invention can be found in the corresponding parts of the method embodiments, which will not be described in detail here.

[0112] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

[0113] It should be noted that the present invention can be implemented in software and / or a combination of software and hardware, for example, can be implemented using an application specific integrated circuit (ASIC), a general purpose computer or any other similar hardware device. In one embodiment, the software program of the present invention can be executed by a processor to implement the steps or functions described above. Similarly, the software program of the present invention (including related data structures) can be stored in a computer readable recording medium, for example, a RAM memory, a magnetic or optical drive or a floppy disk and the like. In addition, some steps or functions of the present invention can be implemented using hardware, for example, as a circuit that cooperates with a processor to perform each step or function.

[0114] In addition, a part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. The program instruction for calling the method of the present invention may be stored in a fixed or removable recording medium, and / or transmitted through a data stream in a broadcast or other signal-bearing medium, and / or stored in a working memory of a computer device that runs according to the program instruction. Here, according to an embodiment of the present invention, a device is included, the device including a memory for storing computer program instructions and a processor for executing program instructions, wherein, when the computer program instruction is executed by the processor, the device is triggered to run the method and / or technical solution based on the aforementioned multiple embodiments of the present invention.

[0115] It is obvious to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive, and the scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved. In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the device claim can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

Claims

1. A vision-based automatic calibration method for pipetting stations, characterized in that: include: Placing a calibration block with a built-in camera on a target plate position that needs to be calibrated in a pipetting station, wherein a crosshair reference coordinate is set on the target plate position; A calibration head with a circular bottom is installed on the pipette, and the pipette with the calibration head is moved to a shooting range where the circular shape of the calibration head can be detected in the photo taken by the camera, which serves as the camera field of view; Based on the calibration head, the target plate position and the camera field of view, a visual internal calibration is performed to obtain the pixel coordinates O2 of the center of the circle, the radius R1 of the circle, the coordinates C1 of the robot of the pipetting station and the offset Offset between the teaching point and the reference coordinates; Based on the pixel coordinates O2 of the center of the circle, the radius R1 of the circle, the coordinates C1 of the robot of the pipetting station, and the offset Offset between the teaching point and the reference coordinates, visual calibration is performed to obtain the calibration value of the target plate position.

2. The method for automatic calibration of the position of a pipetting station based on vision as claimed in claim 1, characterized in that: Based on the calibration head, the target plate position and the camera field of view, visual internal calibration is performed to obtain the pixel coordinates O2 of the center of the circle, the radius R1 of the circle, the coordinates C1 of the robot of the pipetting station and the offset Offset between the teaching point and the reference coordinates, including: Move the pipette with the calibration head to 9 first positions within the field of view of the camera, where the Z axis is fixed and the X axis and the Y axis are different, use the camera on the calibration block to take 9 first images corresponding to each first position, and record the coordinates of the pipetting station robot corresponding to the 9 different first positions; The affine transformation matrix is ​​calculated by using the pixel coordinates of the center of the 9 first images and the corresponding 9 pipetting station robot coordinates; Keeping the Z axis unchanged, move the pipette gun to any position within the field of view of the camera as a teaching point, capture a second image through the camera at the teaching point, and obtain the pixel coordinates O2 of the center of the circle at the bottom of the calibration head at the teaching point, the radius R1 of the circle, and the coordinates C1 of the pipetting station robot based on the second image; measure the distance from the center of the circle at the bottom of the calibration head to the camera; Move the pipette gun to the position of the crosshair reference coordinate of the target plate position, and record the current pipetting station robot coordinate C2; The coordinate deviation between the pipetting station robot coordinate C1 and the pipetting station robot coordinate C2 is calculated to obtain the offset Offset between the teaching point and the reference coordinate.

3. The vision-based automatic calibration method for pipetting stations according to claim 2, characterized in that: The affine transformation matrix is ​​calculated by using the pixel coordinates of the center of the 9 first images and the corresponding 9 pipetting station robot coordinates, including: Each pixel coordinate of the center of the circle corresponds to a pipetting station robot coordinate, and for each pair of points formed by each pixel coordinate of the center of the circle and the corresponding pipetting station robot coordinate, a set of linear equations is established to express the corresponding relationship between the pixel coordinates of the center of the circle and the coordinates of the pipetting station robot, wherein each pair of points corresponds to a set of linear equations, and 9 pairs of points will form 9 sets of linear equations; Based on 9 sets of linear equations, the parameters of the affine transformation matrix are solved by the least squares method; Based on the affine transformation matrix parameters, a 2x3 affine transformation matrix is ​​obtained to transform the pixel points in the image coordinate system into the pipetting station robot coordinate system.

4. The method for automatic calibration of the position of a pipetting station based on vision as claimed in claim 3, characterized in that: The parameters of the affine transformation matrix include 6 parameters of the transformation coefficients of rotation, scaling and translation.

5. The method for automatic calibration of the position of a pipetting station based on vision as claimed in claim 4, characterized in that: The affine transformation matrix is ​​expressed as the following formula (1): Wherein, (x, y) is the pixel coordinate of the center of the first image; (x', y') is the robot coordinate of the pipetting station; [a 11 ,a 12 ,a 21 ,a 22 ] is the linear part of the transformation matrix, which represents the transformation coefficients of rotation and scaling; [t x ,t y ] is a translation vector, representing the translation transformation coefficient of the first image.

6. The vision-based automatic calibration method for pipetting stations according to claim 5, characterized in that: Based on the pixel coordinates O2 of the center of the circle, the radius R1 of the circle, the coordinates C1 of the pipetting station robot, and the offset Offset between the teaching point and the reference coordinate, visual calibration is performed to obtain the calibration value of the target plate position, including: Step S40, selecting any position within the field of view of the camera as the current position of the pipette; Step S41, at the current position of the pipette, use the camera in the calibration block to capture a third image; Step S42, detecting the current radius R2 of the circle at the bottom of the calibration head in the third image by using the Hough circle detection algorithm; Step S43, subtracting the radius R1 of the circle from the current radius R2 of the corresponding circle in the third image to obtain a radius deviation, if the radius deviation is greater than a preset radius deviation threshold, proceed to step S44, otherwise proceed to step S45; Step S44, calculating the offset of the Z axis by the radius deviation, taking the position of the pipette after moving the pipette according to the offset of the Z axis as the current position of the pipette, and returning to step S41; Step S45, at the current position of the pipette, use the camera in the calibration block to capture a fourth image; Step S46, detecting the pixel coordinate O4 of the center of the circle at the bottom of the calibration head in the fourth image by using the Hough circle detection algorithm; Step S47, subtracting the center pixel coordinate O2 from the center pixel coordinate O4 corresponding to the fourth image to obtain a pixel deviation. If the pixel deviation is greater than a preset pixel deviation threshold, proceed to step S48, otherwise proceed to step S49; Step S48, converting the pixel deviation into a pipetting station robot coordinate offset through the affine transformation matrix, taking the position of the pipette after moving the pipette according to the pipetting station robot coordinate offset as the current position of the pipette, and returning to step S45; Step S49, subtracting the offset Offset between the teaching point and the reference coordinate from the X-axis and Y-axis coordinates of the current position of the pipette gun to obtain the X-axis and Y-axis coordinates of the crosshair reference coordinates; Step S50, subtract the Z-axis coordinate of the current position of the pipette gun from the Z-axis coordinate of the pipette station robot coordinate C1, and add the Z-axis coordinate of the pipette station robot coordinate C2 to obtain the Z-axis coordinate of the crosshair reference coordinate of the target plate position.

7. The vision-based automatic calibration method for pipetting stations according to claim 6, characterized in that: In step S44, the Z-axis offset calculation formula is: (the distance from the center of the circle at the bottom of the calibration head to the camera*circle radius deviation / current radius R2).

8. The vision-based automatic calibration method for pipetting stations according to claim 6, characterized in that: In step S48, the formula for converting the pixel deviation to the coordinate offset of the pipetting station robot is as follows: dx′=a 11 dx+a 12 dy dy′:a 21 dx+a 22 of Where (dx, dy) is the image pixel deviation; (dx', dy') is the coordinate offset of the pipetting station robot.

9. A computer-readable storage medium having computer-executable instructions stored thereon, wherein: When the computer executable instructions are executed by a processor, the processor is caused to: perform the method according to any one of claims 1 to 8.

10. A computer device, wherein: include: processor; as well as A memory arranged to store computer executable instructions which, when executed, cause the processor to: perform a method as claimed in any one of claims 1 to 8.

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